from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__a    :	str         =    logging.get_logger(__name__)  # pylint: disable=invalid-name

__a    :	Union[str, Any]         =    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=8				):







				"""simple docstring"""



				__lowercase					     =				height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				__lowercase					     =				width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Optional[int]:

							'''simple docstring'''


							super().__init__()

							self.register_modules(
							    unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__ ,  movq=lowerCAmelCase__ ,  )
							__lowercase					     =				2 ** (len(self.movq.config.block_out_channels		) - 1)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							if latents is None:
											__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  device=lowerCAmelCase__ ,  dtype=lowerCAmelCase__		)
							else:
											if latents.shape != shape:
															raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
											__lowercase					     =				latents.to(lowerCAmelCase__		)

							__lowercase					     =				latents * scheduler.init_noise_sigma
							return latents





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=0		)	->   int:

							'''simple docstring'''


							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

							__lowercase					     =				torch.device(F"cuda:{gpu_id}"		)

							__lowercase					     =				[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
															cpu_offload(lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=0		)	->   Dict:

							'''simple docstring'''


							if is_accelerate_available() and is_accelerate_version('''>=''' ,  '''0.17.0.dev0'''		):
											from accelerate import cpu_offload_with_hook
							else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''		)

							__lowercase					     =				torch.device(F"cuda:{gpu_id}"		)

							if self.device.type != "cpu":
											self.to('''cpu''' ,  silence_dtype_warnings=lowerCAmelCase__		)
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							__lowercase					     =				None
							for cpu_offloaded_model in [self.unet, self.movq]:
											__lowercase     ,	__lowercase					     =				cpu_offload_with_hook(lowerCAmelCase__ ,  lowerCAmelCase__ ,  prev_module_hook=lowerCAmelCase__		)

							# We'll offload the last model manually.
							__lowercase					     =				hook





			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							if not hasattr(self.unet ,  '''_hf_hook'''		):
											return self.device
							for module in self.unet.modules():
											if (
											    hasattr(lowerCAmelCase__ ,  '''_hf_hook'''		)
											    and hasattr(module._hf_hook ,  '''execution_device'''		)
											    and module._hf_hook.execution_device is not None
											):
															return torch.device(module._hf_hook.execution_device		)
							return self.device





			@torch.no_grad()
			@replace_example_docstring(lowerCAmelCase__		)
			def __call__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 1_00 ,  lowerCAmelCase__ = 4.0 ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  )	->   int:

							'''simple docstring'''


							__lowercase					     =				self._execution_device

							__lowercase					     =				guidance_scale > 1.0

							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				torch.cat(lowerCAmelCase__ ,  dim=0		)
							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				torch.cat(lowerCAmelCase__ ,  dim=0		)
							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				torch.cat(lowerCAmelCase__ ,  dim=0		)

							__lowercase					     =				image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
											__lowercase					     =				image_embeds.repeat_interleave(lowerCAmelCase__ ,  dim=0		)
											__lowercase					     =				negative_image_embeds.repeat_interleave(lowerCAmelCase__ ,  dim=0		)
											__lowercase					     =				hint.repeat_interleave(lowerCAmelCase__ ,  dim=0		)

							__lowercase					     =				torch.cat([negative_image_embeds, image_embeds] ,  dim=0		).to(dtype=self.unet.dtype ,  device=lowerCAmelCase__		)
							__lowercase					     =				torch.cat([hint, hint] ,  dim=0		).to(dtype=self.unet.dtype ,  device=lowerCAmelCase__		)
							self.scheduler.set_timesteps(lowerCAmelCase__ ,  device=lowerCAmelCase__		)
							__lowercase					     =				self.scheduler.timesteps

							__lowercase					     =				self.movq.config.latent_channels

							__lowercase     ,	__lowercase					     =				downscale_height_and_width(lowerCAmelCase__ ,  lowerCAmelCase__ ,  self.movq_scale_factor		)

							# create initial latent
							__lowercase					     =				self.prepare_latents(
							    (batch_size, num_channels_latents, height, width) ,  image_embeds.dtype ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  self.scheduler ,  )

							for i, t in enumerate(self.progress_bar(lowerCAmelCase__		)		):
											# expand the latents if we are doing classifier free guidance
											__lowercase					     =				torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

											__lowercase					     =				{'''image_embeds''': image_embeds, '''hint''': hint}
											__lowercase					     =				self.unet(
											    sample=lowerCAmelCase__ ,  timestep=lowerCAmelCase__ ,  encoder_hidden_states=lowerCAmelCase__ ,  added_cond_kwargs=lowerCAmelCase__ ,  return_dict=lowerCAmelCase__ ,  )[0]

											if do_classifier_free_guidance:
															__lowercase     ,	__lowercase					     =				noise_pred.split(latents.shape[1] ,  dim=1		)
															__lowercase     ,	__lowercase					     =				noise_pred.chunk(2		)
															__lowercase     ,	__lowercase					     =				variance_pred.chunk(2		)
															__lowercase					     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
															__lowercase					     =				torch.cat([noise_pred, variance_pred_text] ,  dim=1		)

											if not (
											    hasattr(self.scheduler.config ,  '''variance_type'''		)
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
															__lowercase     ,	__lowercase					     =				noise_pred.split(latents.shape[1] ,  dim=1		)

											# compute the previous noisy sample x_t -> x_t-1
											__lowercase					     =				self.scheduler.step(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  )[0]
							# post-processing
							__lowercase					     =				self.movq.decode(lowerCAmelCase__ ,  force_not_quantize=lowerCAmelCase__		)['''sample''']

							if output_type not in ["pt", "np", "pil"]:
											raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"		)

							if output_type in ["np", "pil"]:
											__lowercase					     =				image * 0.5 + 0.5
											__lowercase					     =				image.clamp(0 ,  1		)
											__lowercase					     =				image.cpu().permute(0 ,  2 ,  3 ,  1		).float().numpy()

							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)